Method for Detecting Phishing Sites

Phishing attacks are a serious threat to the security of confidential data. Traditional user training is not able to fully counter unknown threats. Machine learning is currently the most effective mechanism for detecting phishing sites. Therefore, the presentation of a method in which detection is realized on the basis of fuzzy clustering, which ensures the automated operation of the algorithm without the intervention of an observer, is a relevant topic. The article examines current threats and methods of countering phishing attacks using fake websites. The market for solutions that use machine learning methods to detect phishing sites is analyzed. An improved model for detecting phishing sites based on fuzzy clustering of C-means compromise indicators by combining the closest clusters is proposed, provided that the model efficiency is the highest and the number of clusters is more than two. The proposed model has the potential to become an effective tool for detecting phishing sites and ensuring the security of confidential data. However, further research and improvement of the model, including parameter optimization and the use of additional machine learning techniques, is necessary to achieve optimal results. In general, the article aims to improve the level of protection against phishing attacks by using machine learning techniques and developing a new detection model based on fuzzy clustering. It makes an important contribution to the field of cybersecurity and contributes to ensuring the security of confidential user data from phishing attacks.